33 computational-physics-"https:"-"https:"-"https:"-"https:"-"Ulster-University" Fellowship positions at Monash University in Australia
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the project’s cross-cutting computational and bioanalytical objectives and will involve developing a data base to record and access clinical data from a wide variety of animal species and also to develop a
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campus. This Level A research-only position will contribute to the research program of the Signalling Network Laboratory, undertaking projects focused on specific members of the protein kinase superfamily
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people who discover them The Opportunity Join a dynamic research program funded by the Australian Economic Accelerator (AEA) and the Australian Research Council (ARC) to advance electrochemical biosensors
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neuroscience research environments, embedded within the Alfred Research Alliance and closely connected to leading clinical services. About the Role As a key member of our neurorehabilitation research program
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key part in supporting Australia’s transition to green ironmaking by developing a quantitative framework that connects impurity content, process parameters and microstructural evolution to scalable, low
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and conduct experiments, operate specialised equipment, develop and implement experimental procedures, and support project delivery through effective scheduling and computational analysis. This role
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postgraduate qualification in Data Science / Computer Science (PhD preferred) Strong expertise in Python and/or R, SQL, data engineering and machine learning Experience with EMR systems (Cerner highly desirable
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relationships in organic semiconductor materials, working closely with Professor Chris McNeill in the Department of Materials Science and Engineering, Dr. Amelia Liu in the School of Physics & Astronomy as
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Future Fund–supported program (2026–2030) to reduce addiction, self-harm, and mental ill health. The project integrates 20 years of binational cohort and cross-sectional data with administrative datasets
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in supporting Australia’s transition to green ironmaking by developing a quantitative framework that connects impurity content, process parameters and microstructural evolution to scalable, low